code: def local_feature_impact_graph(self, df):
      df2 = self.data.feature_impact(df)
      names = list(df2["VariableName"])
      new_names = []
      for n in names:
          new_names.append(n.strip("_impact"))
      df2["VariableName"] = new_names
      df2.sort_values(by="Impact_Value", ascending=True, inplace=True)
  
      signs_ = ["+" if i > 0 else "-" for i in df2["Impact_Value"]]
      colors_ = [
          "rgba(41, 128, 185,1.0)" if i > 0 else "rgba(192, 57, 43,1.0)"
          for i in df2["Impact_Value"]
      ]
  
      feature_impact = go.Figure()
      feature_impact.add_trace(
          go.Bar(
              y=list(df2["VariableName"]),
              x=list(df2["Impact_Value"]),
              name="features_impact",
              text=signs_,
              texttemplate="%{x:.2f} ",
              textposition="auto",
              cliponaxis=False,
              orientation="h",
              hovertemplate="Feature: %{y} <br> Impact: %{x:.4f}<extra></extra>",
              marker=dict(
                  color=colors_,
                  line=dict(color="rgba(0, 0, 0, 0.5)", width=0),
              ),
          )
      )
  
      feature_impact.update_layout(
          {"xaxis": {"title": "Impact on Prediction"}}
      )
      feature_impact.update_layout({"yaxis": {"title": "Feature Name"}})
      feature_impact.update_layout(
          template="plotly_white",
          margin={
              "t": 0,
          },
          font=dict(size=10),
      )
  
      return feature_impact, df2
  
file_path: explainx/lib/plotly_graphs.py
imports: from imports import *
  from feature_impact import *
  from feature_importance import *
  from shap_pdp import *
  from summary_plot import *
  from data_for_shap_graphs import *
label_desc: Use image node_id 11 for calling the plotly_graphs obj's underlying member method code with example usage: obj.local_feature_impact_graph(df) and returns: feature_impact,  df2